{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ggplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f314490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284032785)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='cyl')) + geom_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cf/318eijj8bY2Fhs2LAh2tvbI+Ld+0X9iwIAgPcyrZsiLrvssrjssstmahYA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\nogAApPi/Nv+XyuIGqjoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f308bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284824197)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='cyl', fill='blue')) + geom_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AACRJyAIAkCQhCwBAkoQsAABJErIAACRJyAIAkCQhCwBAkoQsAABJErIAACRJyAIAkCQh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\nAQBIkpAFACBJQhYAgCQJWQAAkvT/AAstSLIeIyQkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd33590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284473797)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='cyl')) + geom_bar(color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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trsXgdDpn/WkVZFWc3LxSqVSJp7GmTD5WXVs2m83ya8sKlst5KHEuzsfq52Hun1Zitbys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\n8SjFAAAAMN7/AUpzcjVnDcxoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11156d5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (286591669)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(diamonds, aes(x='clarity', weight='x')) + geom_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r6ye97mg0Oulj5rry8vJxt5PVWBNlZZpwOJzvJcwat98ahokxV5PDbDnHbDnHXM0sR6X4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\nbFlm209/+lN98cUX8ng8Onf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111965390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (287360613)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(diamonds, aes(x='clarity')) + geom_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
